Clustering Dynamic Web Usage Data

International audienceMost classification methods are based on the assumption that data conforms to a stationary distribution. The machine learning domain currently suffers from a lack of classification techniques that are able to detect the occurrence of a change in the underlying data distribution. Ignoring possible changes in the underlying concept, also known as concept drift, may degrade the performance of the classification model. Often these changes make the model inconsistent and regular updatings become necessary. Taking the temporal dimension into account during the analysis of Web usage data is a necessity, since the way a site is visited may indeed evolve due to modifications in the structure and content of the site, or even due to changes in the behavior of certain user groups. One solution to this problem, proposed in this article, is to update models using summaries obtained by means of an evolutionary approach based on an intelligent clustering approach. We carry out various clustering strategies that are applied on time sub-periods. To validate our approach we apply two external evaluation criteria which compare different partitions from the same data set. Our experiments show that the proposed approach is efficient to detect the occurrence of changes

Classification des journées en fonction des radiations solaires sur l'île de la Réunion

National audienceL'objectif de cet article est de montrer les intérêts et les inconvénients de deux approches classificatoires de courbes. La première est basée sur une représentation des courbes sous forme vectorielle, la seconde propose la distance de D'Urso et Vichi qui est basée sur les première et seconde dérivées finies. Cette dernière intègre au mieux les propriétés mathématiques des courbes. Ces deux approches seront appliquées à la classification de sources de production d'énergie de type photovoltaïque

Classification de données Symboliques en présence de règles. Le cas des variables "histogrammes"

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Multi-View Clustering on Relational Data

International audienceClustering is a popular task in knowledge discovery. In this chapter we illustrate this fact with a new clustering algorithm that is able to partition objects taking into account simultaneously their relational descriptions given by multiple dissimilarity matrices. The advantages of this algorithm are threefold: it uses any dissimilarities between objects, it automatically ponderates the impact of each dissimilarity matrice and it provides interpretation tools. We illustrate the usefulness of this clustering method with two experiments. The first one uses a data set concerning handwritten numbers (digitized pictures) that must be recognized. The second uses a set of reports for which we have an expert classification given a priori so we can compare this classification with the one obtained automatically

Un algorithme de classification automatique pour des données relationnelles multi-vues

International audienceThis paper introduces an improvement of a clustering algorithm \citep{decarvalho12} that is able to partition objects taking into account simultaneously their relational descriptions given by multiple dissimilarity matrices. These matrices could have been generated using different sets of variables and dissimilarity functions. This method, which is based on the dynamic hard clustering algorithm for relational data, is designed to provided a partition and a prototype for each cluster as well as to learn a relevance weight for each dissimilarity matrix by optimizing an adequacy criterion that measures the fit between clusters and their representatives. These relevance weights change at each algorithm iteration and are different from one cluster to another. Moreover, various tools for the partition and cluster interpretation furnished by this new algorithm are also presented. Two experiments demonstrate the usefulness of this clustering method and the merit of the partition and cluster interpretation tools. The first one uses a data set from UCI machine learning repository concerning handwritten numbers (digitalized pictures). The second uses a set of reports for which we have an expert classification given a priori.Cet article introduit une amélioration d'un algorithme de classification automatique \citep{decarvalho12} capable de partitionner des objets en prenant en compte de manière simultanée plusieurs matrices de dissimilarité qui les décrivent. Ces matrices peuvent avoir été générées en utilisant différents ensembles de variables et de fonctions de dissimilarité. Cette méthode, basée sur l'algorithme de nuées dynamiques est conçu pour fournir une partition et un prototype pour chaque classe tout en découvrant une pondération pertinente pour chaque matrice de dissimilarité en optimisant un critère d'adéquation entre les classes et leurs représentants. Ces pondérations changent à chaque itération de l'algorithme et sont différentes pour chacune des classes. Nous présentons aussi plusieurs outils d'aide á l'interprétation des groupes et de la partition fournie par cette nouvelle méthode. Deux exemples illustrent l'interêt de la méthode. Le premier utilise des données concernant des chiffres manuscrits (0 à 9) numérisés en images binaires provenant de l'UCI. Le second utilise un ensemble de rapports dont nous connaissons une classification experte donnée à priori